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概要と使いどころ
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
日本語の概要は準備中です。原文の説明を表示しています。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
日本語の概要は準備中です。原文の説明を表示しています。
Anna's Archive integration for academic paper and book retrieval. Search shadow libraries (Library Genesis, Z-Library, Sci-Hub) via unified API with GF(3) balanced caching.
日本語の概要は準備中です。原文の説明を表示しています。
Bridge layer connecting the Agentic AI Foundation (AAIF) governance structure — Linux Foundation stewardship of MCP, goose, AGENTS.md — to the plurigrid/asi skill graph. Formalizes AAIF-compatible skill registration, IPSIE identity profiles, and the cross-protocol interoperability surface. Maps the AAIF platinum member ecosystem (AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, OpenAI) to specific ASI skill integration points.
日本語の概要は準備中です。原文の説明を表示しています。
Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color integration.
日本語の概要は準備中です。原文の説明を表示しています。
Executes a four-phase feature addition workflow (Discovery, Planning, TDD Implementation, Verification) for Python projects. Use when adding a new feature end-to-end — discovering project structure and integration points, drafting a feature spec with MoSCoW-prioritized requirements and BDD acceptance criteria, implementing via test-first TDD cycles, then verifying with ruff lint, ty type checks, and behavior-focused regression and contract coverage.
日本語の概要は準備中です。原文の説明を表示しています。
MkDocs documentation project reference covering CLI commands, mkdocs.yml configuration, Material theme setup, and plugin integration. Bundled references include complete CLI parameters, all mkdocs.yml settings with valid values, Material theme customization options, and plugin configs for mkdocstrings, mermaid2, mkdocs-gen-files, mkdocs-literate-nav, and mkdocs-typer2. Use when initializing a MkDocs site, configuring mkdocs.yml, customizing the Material theme, integrating plugins, building static docs from Markdown, or generating API documentation from Python docstrings.
日本語の概要は準備中です。原文の説明を表示しています。
Guides pytest test architecture for Python 3.11+ using behavioral contracts, risk, and maintenance-adjusted value. Use when choosing unit/integration/property/e2e boundaries, fixture strategy, coverage measurement, or mutation testing. Preserves existing project gates but does not invent test-pyramid ratios, coverage percentages, mutation-score targets, or tests for implementation detail.
日本語の概要は準備中です。原文の説明を表示しています。
Use when diagnosing a Python traceback, failing test, intermittent failure, integration defect, or other unexpected runtime behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Use when building or debugging Typer/Rich CLI applications. Activates on Rich table rendering, console output in non-TTY environments, CliRunner testing with Rich output, snapshot testing, Typer command wiring, exception chain prevention with AppExit/AppExitRich patterns, table width at 80-column wrapping, Progress/Live in non-interactive contexts, stderr/stdout separation, or force_terminal vs width configuration. Grounds AI-generated CLI code in verified correctness patterns and prevents known Typer/Rich integration mistakes.
日本語の概要は準備中です。原文の説明を表示しています。
Use when creating a new skill or updating an existing skill that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Activates on "create a new skill", "add a skill to plugin", or "update existing skill".
日本語の概要は準備中です。原文の説明を表示しています。
Verify that changed components are actually connected into the intended end-to-end system flow. Use when checking imports/exports, registrations, data/control flow, configuration consumption, call paths, and boundary compatibility after implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Decomposes a contextualized plan into atomic, independently executable tasks with complete embedded context, registered through the plan API. Use after SAM Stage 3 Context Integration produces the contextualized plan artifact — when the plan is ready for task generation with CLEAR ordering, CoVe checks, and dependency graphs for parallel execution.
日本語の概要は準備中です。原文の説明を表示しています。
Use when all tasks for a feature are marked COMPLETE — runs holistic quality gates including code review, feature verification, integration check, documentation drift audit and update, and context refinement. Creates follow-up plans when issues are found.
日本語の概要は準備中です。原文の説明を表示しています。
Evaluate and iterate on the SDLC Layer Separation Architecture implementation. Runs validation checks (cross-references, doc completeness, layer metadata, integration points), produces a findings report, and supports iterative fixes. Use when validating first-pass implementation, before claiming layer work is complete, or when improving layer docs/schema.
日本語の概要は準備中です。原文の説明を表示しています。
Reviews LLM integration, prompt templates, model selection, token/cost budgets, evaluation harnesses, structured outputs, retries, and streaming. Checks trust boundaries and task evidence against the selected provider/model contract rather than prescribing universal model tiers or sampling settings.
日本語の概要は準備中です。原文の説明を表示しています。
Agentskill kaizen plugin documentation index. Load when needing to read about cross-platform notes, improvement plans, or DuckDB integration.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze git branches and generate structured MR/PR descriptions with domain-based change categorization — bug fixes, enhancements, technical debt, documentation, testing, build/CI, and non-functional changes. Use when preparing merge request descriptions, pull request bodies, writing changelogs from git diffs, documenting branch changes, or generating release notes. Works with GitHub and GitLab without requiring JIRA or issue tracker integration.
日本語の概要は準備中です。原文の説明を表示しています。
Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.
日本語の概要は準備中です。原文の説明を表示しています。
Select, define, create, inspect, update, attach, detach, and verify reusable Vapi tools through the public API. Use for native call-control tools, supported provider integrations, API Request tools, custom function tools, MCP tools, tool messages, credentials, or configuration-preserving assistant attachment changes.
日本語の概要は準備中です。原文の説明を表示しています。
Create complete documentation sites for projects. Use when asked to: "create docs", "add documentation", "setup docs site", "generate docs", "document my project", "write docs", "initialize documentation", "add a docs folder", "create a docs website". Generates Docus-based sites with search, dark mode, MCP server, and llms.txt integration.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate complex work from an active gpt-6-astra or gpt-6.1-sol Codex session. The detected lead owns decomposition, integration, and verification; Astra keeps the hardest reasoning in the lead, while Sol escalates unusually difficult units to Astra. Both route bounded work to Sol at medium effort, mechanical work to Luna, and can use a cross-vendor Claude peer. Not for routine single-context work. Use when the user explicitly asks to orchestrate, delegate, fan out, parallelize, assign subagents, obtain independent checks, or have Codex act as tech lead.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," or "building many pages for SEO." For auditing existing SEO issues, see seo-audit.
日本語の概要は準備中です。原文の説明を表示しています。
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
日本語の概要は準備中です。原文の説明を表示しています。